Detection of geothermal anomalies using pre-dawn thermal remote sensing data from ECOSTRESS sensor

As energy transition becomes increasingly urgent, remote sensing technologies can contribute by delivering maps of sustainable energy sources. One example of such source are volcanic geothermal systems. Energy from geothermal systems can be produced constantly, regardless of weather conditions, and some countries like Kenya already make use of this advantage, by producing a large proportion of their energy demands from geothermal resources. Using space-borne thermal imagery allows automated mapping of terrains suitable for energy production from geothermal systems, by detecting thermal anomalies associated with presence of a geothermal system.
Detection of geothermal anomalies is a challenging task, because such anomalies are only slightly warmer than their surroundings, and typically rather small. Additional difficulty lies in thermal properties of surfaces, which are being warmed-up by solar radiation. It is therefore advantageous to use nighttime thermal imagery, but early in the night the surfaces are still cooling down which can falsify detections. It would by optimal to use pre-dawn imagery only, because the contrast between geothermal anomalies and their surroundings should be the highest in that moment. However, most available sensors image the areas of interest shortly after sunset.
The launch of ECOSTRESS sensor in 2018 provided new opportunities for global geothermal system exploration from space. ECOSTRESS has a precessing orbit of International Space Station, where it is installed, so data can be acquired at different times of day and night for the same location. Thus, ECOSTRESS can acquire imagery pre-dawn, which opens promising opportunities for global mapping of geothermal anomalies.
ECOSTRESS uses three spectral bands to create Land Surface Temperature and Emissivity datasets, which are provided in spatial resolution of 70 m. Its high spatial resolution provides additional advantage in using ECOSTRESS data for preliminary exploration of geothermal systems.
In our research, we decided to test the feasibility of using pre-dawn ECOSTRESS data for detection of geothermal surface temperature anomalies. The study was conducted in Olkaria region, Kenya, which is administered by KenGen, a company owning geothermal power plants in this area. We compared the results obtained from nighttime imagery (all the acquisition times) to the results obtained from pre- dawn imagery only. We validated both results against auxiliary data which was produced during field work conducted in March 2022. Thus, detection overall accuracy, producer’s- and user’s accuracy, as well as omission and commission error could be calculated.
The main differences between pre-dawn and all-night imagery lays in edges of the anomalies. The geothermally active areas receive heat from solar radiation as well as from the geothermal motor, but the edges of the anomalies seem to cool down during the night. The true size of anomalies is, therefore, visible much more clearly in the pre-dawn imagery. Another interesting result is visible in several locations classified as commission errors in all-night imagery. These areas do not appear as detections in the pre-dawn imagery. These places are areas with a lot of bare rocks, which possibly warm up significantly over the daytime and require long time to cool down due to higher heat capacity and thermal inertia.
The presented results prove that using pre-dawn imagery for geothermal anomaly detection delivers more accurate results than the imagery from different times of day and night. ECOSTRESS pre-dawn imagery prove potential for global mapping of geothermal anomalies and thus contributing to energy transition.